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Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 10684

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Special Issue Editor

Dr. Víctor Fernández-García

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Guest Editor

Institute of Geography and Environment, University of Lausanne, Lausanne, Switzerland
Interests: remote sensing; soil science; vegetation science; fire ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest fires are one of the most important disturbances around the world, producing negative impacts primarily in the provision and regulation of ecosystem services. Furthermore, during the last decade, the magnitude and extension of these fires have grown, making account management more difficult. In this context, remote sensing is a valuable tool to deal with the environmental challenges of fires and to drive solutions. Because of its versatility, the wealth of information it provides, and its rapid advancements in technology, techniques, and platforms, remote sensing is an essential tool for forest management, monitoring, damage analysis, and result reporting with the aim to facilitate post-fire management.

The previous Special Issue ‘Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis’ was a great success. This Special Issue invites studies covering new remote sensing technologies, sensors, data collections, and processing methodologies that can be successfully applied in post-fire damage mapping, ecosystem service recovery, and post-fire decision-making after large forest fires. We welcome submissions that cover but are not limited to:

predictive mapping of post-fire biodiversity patterns in forests using species distribution models and remote sensing data;
three-dimensional mapping by photogrammetry, LiDAR, and SAR in post-fire studies;
using unmanned aerial vehicles (UAV) in post-fire studies;
remote sensing methods to quantify the biophysical parameters of vegetation;
spectral unmixing models applied to the study of the post-fire recovery of vegetation;
hyperspectral imagery applied to the study of soil burn severity and the post-fire recovery of soils;
analysis of fire impacts in the wildland–urban interface (WUI);
estimation of carbon losses in soil and vegetation caused by fires;
methods to estimate forest canopy status and vegetation recovery after fire;
analysis of post-fire erosion, changes in water sediment loads, and water quality using remote sensing methods.

Dr. Víctor Fernández-García
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

fire damage
post-fire
soil
vegetation
landsat
sentinel
MODIS
multispectral
LiDAR
radar

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Published Papers (6 papers)

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26 pages, 12784 KiB

Open AccessArticle

Advanced Deep Learning Approaches for Forecasting High-Resolution Fire Weather Index (FWI) over CONUS: Integration of GNN-LSTM, GNN-TCNN, and GNN-DeepAR

by Shihab Ahmad Shahriar, Yunsoo Choi and Rashik Islam

Remote Sens. 2025, 17(3), 515; https://doi.org/10.3390/rs17030515 - 1 Feb 2025

Viewed by 535

Abstract

Wildfires in the United States have increased in frequency and severity over recent decades, driven by climate change, altered weather patterns, and accumulated flammable materials. Accurately forecasting the Fire Weather Index (FWI) is crucial for mitigating wildfire risks and protecting ecosystems, human health, and infrastructure. This study analyzed FWI trends across the Continental United States (CONUS) from 2014 to 2023, using meteorological data from the gridMET dataset. Key variables, including temperature, relative humidity, wind speed, and precipitation, were utilized to calculate the FWI at a fine spatial resolution of 4 km, ensuring the precise identification of wildfire-prone areas. Based on this, our study developed a hybrid modeling framework to forecast FWI over a 14-day horizon, integrating Graph Neural Networks (GNNs) with Temporal Convolutional Neural Networks (TCNNs), Long Short-Term Memory (LSTM), and Deep Autoregressive Networks (DeepAR). The models were evaluated using the Index of Agreement (IOA) and root mean squared error (RMSE). The results revealed that the Southwest and West regions of CONUS consistently exhibited the highest mean FWI values, with the summer months demonstrating the greatest variability across all climatic zones. In terms of model performance on forecasting, Day 1 results highlighted the superior performance of the GNN-TCNN model, achieving an IOA of 0.95 and an RMSE of 1.21, compared to the GNN-LSTM (IOA: 0.93, RMSE: 1.25) and GNN-DeepAR (IOA: 0.92, RMSE: 1.30). On average, across all 14 days, the GNN-TCNN outperformed others with a mean IOA of 0.885 and an RMSE of 1.325, followed by the GNN-LSTM (IOA: 0.852, RMSE: 1.590) and GNN-DeepAR (IOA: 0.8225, RMSE: 1.755). The GNN-TCNN demonstrated robust accuracy across short-term (days 1–7) and long-term (days 8–14) forecasts. This study advances wildfire risk assessment by combining descriptive analysis with hybrid modeling, offering a scalable and robust framework for FWI forecasting and proactive wildfire management amidst a changing climate. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)

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18 pages, 3550 KiB

Open AccessArticle

Wildfire Severity to Valued Resources Mitigated by Prescribed Fire in the Okefenokee National Wildlife Refuge

by C. Wade Ross, E. Louise Loudermilk, Joseph J. O’Brien, Steven A. Flanagan, Grant Snitker and J. Kevin Hiers

Remote Sens. 2024, 16(24), 4708; https://doi.org/10.3390/rs16244708 - 17 Dec 2024

Viewed by 705

Abstract

Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to highly valued resources and assets (HVRAs)—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and HVRAs impacted by the 2017 West Mims event, which burned across both prescribed-fire treated and untreated areas within the Okefenokee National Wildlife Refuge. Wildfire severity was quantified using the differenced normalized burn ratio (dNBR) index, while treatment records were used to calculate the prescribed frequency and post-treatment duration, which is defined as the time elapsed between the last treatment and the West Mims event. A generalized additive model (GAM) was fit to model dNBR as a function of post-treatment duration, fire frequency, and vegetation type. Although dNBR exhibited considerable heterogeneity both within and between HVRAs and vegetation classes, areas treated with prescribed fire demonstrated substantial reductions in burn severity. The beneficial effects of prescribed fire were most pronounced within approximately two years post-treatment with up to an 88% reduction in mean wildfire severity. However, reductions remained evident for approximately five years post-treatment according to our model. The mitigating effect of prescribed fire was most pronounced in Introduced Upland Vegetation-Shrub, Eastern Floodplain Forests, and Longleaf Pine Woodland when the post-treatment duration was within 12 months. Similar trends were observed in areas surrounding red-cockaded woodpecker nesting sites, which is an HVRA of significant ecological importance. Our findings support the frequent application of prescribed fire (e.g., one- to two-year intervals) as an effective strategy for mitigating wildfire severity to HVRAs. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)

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22 pages, 10559 KiB

Open AccessArticle

Development of an Algorithm for Assessing the Scope of Large Forest Fire Using VIIRS-Based Data and Machine Learning

by Min-Woo Son, Chang-Gyun Kim and Byung-Sik Kim

Remote Sens. 2024, 16(14), 2667; https://doi.org/10.3390/rs16142667 - 21 Jul 2024

Cited by 1 | Viewed by 1811

Abstract

Forest fires pose a multifaceted threat, encompassing human lives and property loss, forest resource destruction, and toxic gas release. This crucial disaster’s global occurrence and impact have risen in recent years, primarily driven by climate change. Hence, the scope and frequency of forest fires must be collected to establish disaster prevention policies and conduct relevant research projects. However, some countries do not share details, including the location of forest fires, which can make research problematic when it is necessary to know the exact location or shape of a forest fire. This non-disclosure warrants remote surveys of forest fire sites using satellites, which sidestep national information disclosure policies. Meanwhile, original data from satellites have a great advantage in terms of data acquisition in that they are independent of national information disclosure policies, making them the most effective method that can be used for environmental monitoring and disaster monitoring. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite has worldwide coverage at a daily temporal resolution and spatial resolution of 375 m. It is widely used for detecting hotspots worldwide, enabling the recognition of forest fires and affected areas. However, information collection on affected regions and durations based on raw data necessitates identifying and filtering hotspots caused by industrial activities. Therefore, this study used VIIRS hotspot data collected over long periods and the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to develop ST-MASK, which masks said hotspots. By targeting the concentrated and fixed nature of these hotspots, ST-MASK is developed and used to distinguish forest fires from other hotspots, even in mountainous areas, and through an outlier detection algorithm, it generates identified forest fire areas, which will ultimately allow for the creation of a global forest fire watch system. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)

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13 pages, 39030 KiB

Open AccessArticle

The Forest Fire Dynamic Change Influencing Factors and the Impacts on Gross Primary Productivity in China

by Lili Feng and Wenneng Zhou

Remote Sens. 2023, 15(5), 1364; https://doi.org/10.3390/rs15051364 - 28 Feb 2023

Cited by 4 | Viewed by 2031

Abstract

Forest fire as a common disturbance has an important role in the terrestrial ecosystem carbon cycling. However, the causes and impacts of longtime burned areas on carbon cycling need further exploration. In this study, we exploit Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large-scale forest fire dynamic monitoring in China. Band 2, band 4, band 6, and band 7 of MOD09A1 were selected as the most sensitive bands for calculating the Normalized Difference Fire Index (NDFI) to effectively estimate fire burned area. The Convergent Cross Mapping (CCM) algorithm was used to analyze the causes of the forest fire. A trend analysis was used to explore the impacts of forest fire on Gross Primary Productivity (GPP). The results show that the burned area has an increased tendency from 2009 to 2018. Forest fire is greatly influenced by natural factors compared with human factors in China. But only 30% of the forest fire causes GPP loss. The loss is mainly concentrated in the northeast forest region. The results of this study have important theoretical significance for vegetation restoration of the burned area. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)

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36 pages, 15431 KiB

Open AccessArticle

Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model

by Ritu Taneja, Luke Wallace, Samuel Hillman, Karin Reinke, James Hilton, Simon Jones and Bryan Hally

Remote Sens. 2023, 15(5), 1273; https://doi.org/10.3390/rs15051273 - 25 Feb 2023

Cited by 1 | Viewed by 2495

Abstract

The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the current operational approach (visual fuel assessment) for seven sites across south-eastern Australia. These point-based metrics were then up-scaled to a continuous fuel map, an area relevant to fire management using random forest modelling, with predictor variables derived from Airborne Laser Scanning (ALS), Sentinel 2A images, and climate and soil data. The model trained and validated with TLS observations (R

^{2}

= 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R

^{2}

= −0.1 for the cover of both fuel layers). Models for height derived from TLS observations exhibited low-to-moderate performance for the near-surface (R

^{2}

= 0.23) and canopy layers (R

^{2}

= 0.25). The results from this study provide practical guidance for the selection of training data sources and can be utilised by fire managers to accurately generate fuel maps across an area relevant to operational fire management decisions. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)

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19 pages, 25833 KiB

Open AccessTechnical Note

Wildfire Burnt Area Severity Classification from UAV-Based RGB and Multispectral Imagery

by Tomás Simes, Luís Pádua and Alexandra Moutinho

Remote Sens. 2024, 16(1), 30; https://doi.org/10.3390/rs16010030 - 20 Dec 2023

Cited by 3 | Viewed by 2070

Abstract

Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas and their severity using RGB and multispectral aerial imagery captured by an unmanned aerial vehicle. Datasets containing features computed from multispectral and/or RGB imagery were generated and used to train and optimize support vector machine (SVM) and random forest (RF) models. Hyperparameter tuning was performed to identify the best parameters for a pixel-based classification. The findings demonstrate the superiority of multispectral data for burnt area and burn severity classification with both RF and SVM models. While the RF model achieved a 95.5% overall accuracy for the burnt area classification using RGB data, the RGB models encountered challenges in distinguishing between mildly and severely burnt classes in the burn severity classification. However, the RF model incorporating mixed data (RGB and multispectral) achieved the highest accuracy of 96.59%. The outcomes of this study contribute to the understanding and practical implementation of machine learning techniques for assessing and managing burnt areas. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)

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